class_docstr: Principal Component Analysis (PCA).
  
  Parameters
  ----------
  rescale_with_mean
      Whether or not to subtract each column's mean before performing SVD.
  rescale_with_std
      Whether or not to standardize each column before performing SVD.
  n_components
      The number of principal components to compute.
  n_iter
      The number of iterations used for computing the SVD.
  copy
      Whether nor to perform the computations inplace.
  check_input
      Whether to check the coherence of the inputs or not.
docstr: Computes the row principal coordinates of a dataset.
  
  Same as calling `row_coordinates`. This is just for compatibility with
  scikit-learn.
docstr_tok: ["Computes","the","row","principal","coordinates","of","a","dataset",".","Same","as","calling","`","row_coordinates","`",".","This","is","just","for","compatibility","with","scikit-learn","."]
code: def transform(self, X, as_array=False):
      """Computes the row principal coordinates of a dataset.
  
      Same as calling `row_coordinates`. This is just for compatibility with
      scikit-learn.
  
      """
      self._check_input(X)
      rc = self.row_coordinates(X)
      return rc.to_numpy() if as_array else rc
  
imports: from __future__ import annotations
  import functools
  import altair
  import numpy
  import pandas
  import sklearn.base
  import sklearn.utils
  from sklearn import preprocessing
  from prince import svd, utils
label_desc: Use image node_id 7 for calling the PCA obj's underlying member method code with example usage: obj.transform(X, as_array) and returns: unknown